Michigan COVID Data

Get link for data:

from urllib.request import urlopen

from bs4 import BeautifulSoup

# Get data into pandas df
URL = "https://www.michigan.gov/coronavirus/0,9753,7-406-98163_98173---,00.html"
HTML = urlopen(URL).read().decode("utf-8")
start_index = HTML.find("shortdesc")
end_index = HTML.find("footerArea")
data = HTML[start_index:end_index]
soup = BeautifulSoup(data, features="html.parser")
links = [link.get('href') for link in soup.find_all('a')]
finallink = "https://michigan.gov" + \
    [i for i in links if "by_Date" in i][0]

Download data:

temp <- tempfile()
download.file(py$finallink, destfile = temp)
mi_data <- readxl::read_excel(temp)

Clean data:

mi_data = r.mi_data
max_date = max(mi_data["Updated"])
agg_data = mi_data.groupby(["Date"], as_index=False).sum()

date_update = format(py$max_date, '%d %b %Y')
mi_cases_by_day = py$agg_data
head(mi_cases_by_day)

##                  Date Cases Deaths Cases.Cumulative Deaths.Cumulative
## 1 2020-02-29 19:00:00    14      0               14                 0
## 2 2020-03-01 19:00:00    13      1               27                 1
## 3 2020-03-02 19:00:00    22      0               49                 1
## 4 2020-03-03 19:00:00    24      0               73                 1
## 5 2020-03-04 19:00:00    26      0               99                 1
## 6 2020-03-05 19:00:00    42      0              141                 1

Initial Data Visualization:

plot_ly(
  mi_cases_by_day,
  x = ~Date,
  y = ~Cases
)

With 7 day moving average and deaths:

mi_cases_by_day <- mi_cases_by_day %>%
  mutate(
    cases_ma = rollapply(Cases, 7, mean, align = "center", fill = 0),
    deaths_ma = rollapply(Deaths, 7, mean, align = "center", fill = 0)
  )

ay <- list(tickfont = list(color = "red"),
  overlaying = "y",
  side = "right",
  title = "Deaths")

plot_ly(mi_cases_by_day,x = ~Date) %>% 
  # Cases
  add_trace(y = ~Cases, alpha = .6, name = "Cases", type = "scatter", 
            mode = 'markers') %>%
  # Cases MA
  add_lines(y = ~cases_ma, alpha = .8, name = "Cases MA", mode = 'markers') %>%
  # Deaths
  add_trace(name = "Deaths", yaxis = "y2", alpha = .15, y = ~Deaths, x = ~Date,
            color = I("red"), type = "scatter", mode = 'markers') %>%
  # Deaths MA
  add_lines(name = "Deaths MA", yaxis = "y2", y = ~deaths_ma, x = ~Date,
            line = list(color = I("red")), alpha = .8/4, mode = 'markers') %>%
  layout(
    title = "Michigan COVID Cases/Deaths<br>With 7-day Moving Average",
    yaxis2 = ay, legend = list(x = 0.6, y = 0.9),margin = list(r = 50, t = 50)
  ) 

Reflection

What ideas/suggestions from Claus Wilke’s helped shape your visualization?

Storytelling/comparison drove what I was after. Here, I’m trying to show how COVID deaths follow COVID cases, and choices made in this visualization are made to show that comparison.

Is there anything more you wish you could do with this data?

Nothing. I wish I could have made cases and deaths more distinguishable on the plot, but plotly created lots of frustrations in differentiating the two.

What were the most interesting or frustrating technical aspects of doing this?

As mentioned before, making more clear the difference between deaths and cases would have been nice.